AI Platform Engineer

Role Overview

The AI Platform Engineer is responsible for supporting, developing, and operating the foundational enterprise AI platform, enabling reliable and secure AI services, solutions, and autonomous agents on cloud infrastructure. This position involves hands-on work encompassing implementation, integration, deployment, and daily management of AI platforms centered on cloud infrastructure tailored for AI workloads.

Key Responsibilities

• Design, configure, and maintain cloud resources including compute, GPU acceleration, networking, storage, containers, and essential platform services that support enterprise AI platforms and agents.
• Manage runtime environments and deployment frameworks necessary to run AI models, services, and agentic applications in the cloud.
• Provide operational support for enterprise AI platforms, including configuration and administration tasks.
• Work with Azure AI Services such as Azure OpenAI, AI Foundry, MCP gateways, APIs, connectors, and other integration technologies.
• Develop and maintain AI agents that securely interact with approved enterprise data systems, APIs, and tools.
• Facilitate AI development teams by supporting AI coding assistants like Claude Code, OpenAI Codex, GitHub Copilot, and Cursor.
• Collaborate with business units to identify, evaluate, prioritize, and onboard relevant AI use cases.
• Support corporate AI implementations across productivity tools, knowledge management, workflow automation, and department-specific AI agent solutions.
• Oversee centralized production deployment of AI use cases, including testing phases, monitoring, release readiness, documentation, and operational transfers.
• Coordinate with Cloud and Data Platform teams to ensure AI solutions are scalable, secure, and governed appropriately.
• Participate in AI platform evaluations, tool and model assessments, and help shape future technology roadmaps.
• Monitor the health, performance, usage, cost, and access of AI platform infrastructure; respond promptly to operational incidents.
• Diagnose and resolve issues related to AI platforms, infrastructure, integrations, and data access.
• Maintain comprehensive technical documentation, operational procedures, runbooks, and records of AI solutions.
• Ensure all AI infrastructure and implementations comply with enterprise standards for security, data governance, responsible AI practices, and operational policies.
• Contribute to ongoing improvements through automation, reusable solution patterns, and platform enhancements.

Required Qualifications & Experience

• Bachelor’s degree in Computer Science, IT, Engineering, Artificial Intelligence, Data Science, or a closely related field.
• Between 6 to 8 years of practical experience in cloud technology, data platforms, AI systems, integration, automation, or enterprise platform management.
• Hands-on experience with Microsoft Azure cloud services, particularly AI-related offerings and cloud infrastructure provisioning.
• Proficiency in setting up cloud compute, networking, storage, containers, and platform components to support AI workloads.
• Knowledge of APIs, integration layers, connectors, and enterprise-wide connectivity frameworks.
• Understanding of AI platforms, autonomous agents, prompt-driven AI applications, and corporate AI use cases.
• Basic familiarity with data access methods, identity and access management, security controls, and governance protocols.
• Strong problem-analysis, troubleshooting ability, and solution orientation.
• Capability to collaborate effectively with both technical teams and business stakeholders.

Preferred Skills

• Experience using Azure AI Services, Azure OpenAI, AI Foundry, and Copilot or comparable enterprise AI ecosystems.
• Knowledge of MCP gateways, AI agents, tool automation, workflow orchestration, and agent-driven platforms.
• Familiarity with AI programming assistants including Claude Code, OpenAI Codex, GitHub Copilot, and Cursor.
• Expertise in building AI infrastructure using cloud accelerated compute (GPUs), containers/Kubernetes, networking, and model hosting techniques.
• Exposure to enterprise data platforms such as Snowflake, Power BI, and Azure Data Factory.
• Experience in operationalizing AI solutions, automation tools, and integration frameworks.
• Working knowledge of Infrastructure as Code, automation paradigms, and CI/CD pipelines for AI environments.
• Awareness of principles around responsible AI usage, governance frameworks, model validation, and AI risk mitigation.
• Skill in producing detailed technical documents, support runbooks, reusable architectures, and user enablement content.

Preferred Certifications

• Microsoft Azure AI Engineer certification.
• Microsoft Azure infrastructure or administrator-related certifications.
• Certifications in AI, data science, cloud technologies, integration, automation, or security domains.
• Snowflake certification or credentials related to enterprise data platforms are advantageous.

Success Metrics

• Establishment of dependable, secure, and scalable cloud infrastructure for AI platforms and agents.
• Effective onboarding and sustained support of enterprise AI applications.
• Consistent, secure, and well-managed operation of AI platforms and autonomous agents.
• Smooth transition of AI prototypes into fully operational services.
• Enhanced AI adoption, usability, and delivered business value.
• Prompt resolution of AI systems' infrastructure, integration, and data access challenges.
• Compliance with enterprise AI governance, security protocols, and operational standards.